MétaCan
Menu
Back to cohort
Record W2330986929 · doi:10.1002/app.43624

Effect of unfunctionalized and HNO<sub>3</sub>‐functionalized MWCNT on the mechanical and electrical performances of PEMFC bipolar plates

2016· article· en· W2330986929 on OpenAlexafffund
Nafaa Athmouni, Frej Mighri, Saïd Elkoun

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversité de SherbrookeUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialPolyvinylidene fluorideCarbon nanotubeProton exchange membrane fuel cellCompatibilizationCarbon blackNanocompositeFlexural strengthGraphiteSurface modificationDispersion (optics)PolyethyleneChemical engineeringPolymerPolymer blendFuel cellsCopolymer

Abstract

fetched live from OpenAlex

ABSTRACT This study aims at developing lightweight and high performance electrically conductive nanocomposites for proton exchange membrane fuel cell (PEMFC) bipolar plates (BPPs). These composites were made from an optimized co‐continuous mixture of Polyethylene terephthalate (PET) and polyvinylidene fluoride (PVDF) reinforced with highly conductive carbon additives composed of carbon black (CB) and synthetic graphite (GR). Multiwall carbon nanotubes (MWCNT) were functionalized then used to improve BPPs electrical conductivity and their mechanical properties, such as flexural and impact strengths. It was observed that the best BPP prototype was obtained using nitric acid (HNO 3 )‐functionalized MWCNT. The latter led to the smothest BPP surface, the lowest through‐plane resitivity (0.12 Ω cm) and the highest impact and flexural strengths. These results are attributed to the improved dispersion of the functionalized MWCNT, a result of their best compatibilization with the (PET/PVDF) polymeric phase. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133 , 43624.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.183
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied Polymer ScienceSame topicFuel Cells and Related MaterialsFrench-language works237,207